Agentic AI Workflow ROI 2027 Calculator
Calculate ROI of agentic AI workflows — autonomous agents that complete multi-step tasks (research, data extraction, sales outreach, ticket triage). Account for token cost per agent run, success rate, human review overhead, and the cost of failed runs.
Why Agentic ROI Math Is Different
Single-prompt LLM calls cost pennies. Agentic workflows cost dollars per run — each task is 5-50 internal LLM calls plus tool invocations. A single "research this prospect and draft an outreach email" agent run costs USD 0.50-3.00 in tokens. Multiply by thousands of runs per month and the AI bill is meaningful. The ROI is real only when the agent reliably completes tasks that humans take 15-60 minutes to do.
Agent ROI Formula
Net Savings = Successful Runs × (Human Time × Hourly Cost - Agent Token Cost) - Failed Runs × Wasted Cost
Success Rate is the dominant variable — at 60% success an agent earns half what it does at 95%.
Realistic Success Rates by Workflow
2025 production data (LangChain State of Agents Report, Anthropic Computer Use benchmarks): structured data extraction agents hit 85-95 percent success on clean schemas, sales prospect research agents hit 70-85 percent, customer ticket triage agents hit 75-90 percent, autonomous coding agents (SWE-bench class) hit 40-60 percent on real bugs, browser automation agents hit 50-75 percent on common SaaS UIs. Success drops sharply on novel UIs or ambiguous goals.
The Cost of Failed Runs
Failed agent runs still cost tokens — usually 70-100 percent of a successful run because the agent kept trying. Plus, a human has to either redo the work or audit what the agent broke. Budget that a failed run costs 1.5-2x a successful run when you include human cleanup. This is why success rate matters more than per-run token cost in agentic ROI.
Building the Business Case
Use real measured success rates from a 100-run pilot, not vendor claims. Include token cost per attempted run (not just successful). Add human-in-the-loop review time for high-stakes tasks. Subtract human-equivalent labor. Year-one ROI of 200-600 percent is realistic for structured agents (extraction, triage); under 100 percent for browser automation in 2026. Re-evaluate annually as model capability climbs.
Sources: LangChain State of Agents Report 2025, Anthropic Computer Use benchmarks 2025, SWE-bench leaderboard April 2026, Sequoia AI Agent Economics 2025. Last updated: April 2026.